The present invention is directed to an automated method and apparatus for evaluating fibers in a neurological system. In particular, the invention is directed to an automated method and apparatus that employs image analysis techniques to identify and count the number of myelinated nerve fibers in a nerve cross-section.
The problem of studying nerve fiber cross sections quantitatively arises frequently in the field of neuroscience. Parameters such as axon diameter, myelin sheath thickness, eccentricity and nerve fiber density are useful in many different research areas. Measurements of axon diameters and myelin sheath thicknesses have proven to be of great importance in developing mathematical models to describe the conduction properties of myelinated nerve fibers. The conduction velocity of nerve impulses in a nerve fiber can be estimated from the axon diameter, the myelin sheath thickness, and the internode distances.
By performing such measurements on a statistically significant number of nerve fibers in a nerve bundle, and analyzing the distributions of fiber size and shape, neurologists can also draw conclusions about the anatomy and physiology of the nervous system. This information is significant in assessing the damage to a nerve due to natural or experimental causes.
The diameters of the axons and the thicknesses of the myelin sheaths have previously been measured using manual techniques and the results entered into a computer for processing and storage. Manual measurement, however, is a very time-consuming process because such analyses involves a large number of nerve fibers, usually in the range of 1,000 to 3,000 for each transverse nerve section. Human errors are unavoidable in such a laborious task. Moreover, manual observation of objects seen under a microscope often calls for subjective judgments and decisions which causes inconsistent results when several microscope operators are employed on a single project.
Attempts have been made in the past to automate the process described above. For example, Belson, Dudley and Ledley reported in 1968 the use of computer to automatically measure the size of neurons. The system employed an IBM 7094 computer equipped with a FIDAC scanning instrument. See, "Automatic Computer Measurements of Neurons" by M. Belson et al., Pattern Recognition, 1:119-128, 1986"; and "High-speed Automatic Analysis of Biomedical Pictures", by R. S. Ledley, Science, 146:216-223, 1964. The system was designed to measure the size of the nucleus and the perikaryon of the neuron, from which information the cell volume is derived.
In 1975, Dunn, O'Leary and Kumley described a system developed to count nerve fibers. See, "Quantitative Analysis of Micrographs by Computer Graphics", R. F. Dunn et al., Journal of Microscopy, 105:205-213, 1975; and "On-line Computerized Entry and Display of Nerve Fiber Cross Sections Using Single or Segmented Histological Records", Computer and Biomedical Research, 9:229-237, 1976. Each fiber in this system was characterized by eight points input manually by the operator through a graphics tablet. Four of those points were used to indicate the outer boundary of the myelin sheath and the remaining four were used for the boundary of the axon. The computer system then searched for the best set of two concentric circles to represent that particular fiber. The process, however, involved mostly manual tasks and the computer was not responsible for the detection of nerve fibers in an image or the shape analysis of the nerve fibers. Instead, the computer merely searched for the set of concentric circles which best matched the eight points input by the operator. Thus a mistake made by the operator would result in an incorrect output from the computer.
A similar method was used by Geckle and Jenkins to automatically trace the boundary of the axon. See, "Computer-aided Measurements of Transverse Axon Sections for Morphology Studies", by W. J. Geckle and R. E. Jenkins, Computer and Biomedical Research, 16:287-299, 1983. The user, however, has to provide an arbitrary starting point somewhere inside the axon via a digitizing tablet. In other words, the detection of the nerve fibers is manual, while the shape of nerve fibers is traced automatically. The algorithm used is a variation to the blob counting method described in Computer Vision, by D. H. Ballard and C. M. Brown, Prentice-Hall, Englewood Cliffs, N.J.
A more sophisticated system has been developed by Ellis, Rosen and Cavanagh. See, "Automated Measurement of Peripheral Nerve Fibers in Transverse Section", by T. J. Ellis et al., Journal of Biomedical Engineering, 2:272-280, 1980; and "A Programmable Flying-spot Microscope and Picture Pre-processor", by M. J. Eccles et al., Journal of Microscopy, 106:33-42, 1976. This system attempts to locate the nerve fibers by the blob counting algorithm, then it examines each located object to determine whether or not it is a conjugated set of nerve fibers. If it is, the object is segmented. On the other hand, if the object is determined to be a single fiber or an inter-fiber hole, a circularity test decides whether it should be classified as a fiber, based on the assumption that axons in general are circular and that inter-fiber holes usually contain irregular protrusions and can be rejected by testing for circularity.
The different schemes described above were designed to deal with one very special category of histological slides--those containing circular objects. A certain degree of automation is achieved in each method, but all of them require manual inputs of some sort which is undesirable for the reasons previously stated. Full automation of the process, however, presents a very difficult set of problems that have not been addressed by the conventional methods described above.
In general, the procedure of analyzing the histological slide images can be broken down into two parts. First, the objects have to be identified. This is difficult to automate because the computer has to be programmed to understand an image which is no more than a very large set of pixels with different intensity values, requiring immense computational effort and capacity. Second, morphological data are obtained from the identified objects, a step which is very dependent on the success of the identification step.
One of the similarities in most of the previous attempts is to start with a manual thresholding, which converts a gray level image into a one-bit image--an image which consists only of boundary (myelin sheath) and non-boundary (axon or intercellular space) pixels. The advantage in using manual thresholding is that analyzing one-bit images is much easier than analyzing gray scale images since the axon boundaries in 1-bit images are clearly defined. However, manual thresholding has its own shortcomings. Namely, the results of manual thresholding are undesirably inconsistent and not reproducible in general.
The detection of the objects in all the previous works described requires successful thresholdings. The objects can easily be detected by using blob counting if they are nicely separated from each other. This is usually not the case in practice, however, as the images of the nerve fibers often appear to be clumped together due to the high packing density of the nerve fibers and the limited resolving power of the light microscope. A segmentation algorithm must therefore be employed as each cluster will be identified as one object using blob counting.
The above described situation can be improved by using electron micrographs instead of light micrographs. See, "Morphometric Studies on the Studies on the Relationship Between Myelin Sheath and Axon Area in the Sciatic Nerve of Adult Rats", by J Ashami et al., Anatomischer Anzeiger, 140:52-61, 1976. A higher magnification, however, requires a larger number of slides to cover the same region and therefore decreases the efficiency of the system. The location of the objects also turns out to be quite complicated and time-consuming.
Of the four groups described above, only Ellis et al. have attempted to solve this problem. Their algorithm segments an object based on the local curvature along the boundary. The local curvature is first smoothed by a convolution procedure as described in "Analysis of Digitized Boundaries of Planar Objects:, by M. J. Eccles et al., Pattern Recognition, 9:31-41, 1977. Turning points of local curvatures are identified as nodes. Nodes are matched to specify lines along which the object is segmented. The matching is dependent on the orientation, and the absolute distance between, the nodes.
There are several disadvantages associated with the above-described procedure. For example, it is not clear how many smoothing convolutions are needed. Further, the segmentation algorithm assumes the boundaries of the objects to be continuous. The segmentation algorithm, however, is employed after a single level manual thresholding that has been found to produce a significant amount of discontinuities in the object boundaries. Measurement of identified objects is also the last part of the automatic analysis which involves boundary tracing that traditionally employs the blob analysis. Obviously, if segmentation were unsatisfactory, the blob counting that followed would be unsuccessful.
It is therefore an object of the present invention to overcome the deficiencies of the prior art methods and systems discussed above by providing a system that is capable of digitizing images of histological slides from the cross-section of cochlear nerves, counting the number of myelinated nerve fibers in each slide, obtaining morphological statistics about the nerve fibers (including axon area, myelin sheath thickness, fiber eccentricity, etc.) and assessing the damage of cochlear nerves due to natural or experimental causes, including damages induced by electrical stimulation as in cochlear implants.
A further object of the invention is to provide a system that can employ a relatively low cost, general purpose computer and use the lowest possible magnification in order to include as many nerve fibers in one frame as possible, so that the number of frames needed to be processed will be reduced to a minimum.
A still further object of the invention is to provide a system that can accomplish the above-noted function at a reasonable speed and accuracy, and with a reduction in the amount of human inputs.